How to plot CSV data using Matplotlib and Pandas in Python?

To plot CSV data using Matplotlib and Pandas in Python, we can read CSV files directly into a DataFrame and create visualizations. This approach combines the data manipulation power of Pandas with Matplotlib's plotting capabilities.

Creating Sample CSV Data

First, let's create a sample CSV file to demonstrate the plotting process ?

import pandas as pd
import matplotlib.pyplot as plt

# Create sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [23, 25, 22, 24, 26],
    'Marks': [85, 92, 78, 88, 95]
}

df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)

Sample DataFrame:
      Name  Age  Marks
0    Alice   23     85
1      Bob   25     92
2  Charlie   22     78
3    Diana   24     88
4      Eve   26     95

Basic Line Plot

Plot Age and Marks columns with Name as the index ?

import pandas as pd
import matplotlib.pyplot as plt

# Create sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [23, 25, 22, 24, 26],
    'Marks': [85, 92, 78, 88, 95]
}

df = pd.DataFrame(data)

# Set figure size
plt.figure(figsize=(10, 6))

# Set Name as index and plot
df.set_index('Name').plot(kind='line', marker='o')

plt.title('Student Age and Marks')
plt.ylabel('Values')
plt.legend(['Age', 'Marks'])
plt.grid(True)
plt.show()

Bar Chart Visualization

Create a bar chart to compare student marks ?

import pandas as pd
import matplotlib.pyplot as plt

# Create sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [23, 25, 22, 24, 26],
    'Marks': [85, 92, 78, 88, 95]
}

df = pd.DataFrame(data)

# Create bar chart
plt.figure(figsize=(8, 6))
plt.bar(df['Name'], df['Marks'], color='skyblue', edgecolor='navy')

plt.title('Student Marks Comparison')
plt.xlabel('Student Name')
plt.ylabel('Marks')
plt.xticks(rotation=45)
plt.grid(axis='y', alpha=0.3)
plt.show()

Multiple Plot Types

Create subplots to display different visualizations ?

import pandas as pd
import matplotlib.pyplot as plt

# Create sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Age': [23, 25, 22, 24, 26],
    'Marks': [85, 92, 78, 88, 95]
}

df = pd.DataFrame(data)

# Create subplots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# Line plot
df.set_index('Name')['Marks'].plot(kind='line', ax=ax1, marker='o', color='red')
ax1.set_title('Marks Trend')
ax1.set_ylabel('Marks')
ax1.grid(True)

# Scatter plot
ax2.scatter(df['Age'], df['Marks'], color='green', s=100)
ax2.set_title('Age vs Marks')
ax2.set_xlabel('Age')
ax2.set_ylabel('Marks')
ax2.grid(True)

plt.tight_layout()
plt.show()

Key Features

• Direct DataFrame plotting − Use df.plot() for quick visualizations
• Index setting − Use set_index() to define x-axis labels
• Plot customization − Add titles, labels, and styling options
• Multiple chart types − Support for line, bar, scatter, and other plot types

Conclusion

Pandas and Matplotlib provide a powerful combination for visualizing CSV data. Use df.plot() for quick plots or combine with Matplotlib functions for advanced customization and multiple subplot layouts.